A composite electrodynamic mechanism to reconcile spatiotemporally resolved exciton transport in quantum dot superlattices.

Quantum dot (QD) solids are promising optoelectronic materials; further advancing their device functionality requires understanding their energy transport mechanisms. The commonly invoked near-field Förster resonance energy transfer (FRET) theory often underestimates the exciton hopping rate in QD solids, yet no consensus exists on the underlying cause. In response, we use time-resolved ultrafast stimulated emission depletion (STED) microscopy, an ultrafast transformation of STED to spatiotemporally resolve exciton diffusion in tellurium-doped cadmium selenide-core/cadmium sulfide-shell QD superlattices. We measure the concomitant time-resolved exciton energy decay due to excitons sampling a heterogeneous energetic landscape within the superlattice. The heterogeneity is quantified by single-particle emission spectroscopy. This powerful multimodal set of observables provides sufficient constraints on a kinetic Monte Carlo simulation of exciton transport to elucidate a composite transport mechanism that includes both near-field FRET and previously neglected far-field emission/reabsorption contributions. Uncovering this mechanism offers a much-needed unified framework in which to characterize transport in QD solids and additional principles for device design.

Size-Dependent Suppression of Molecular Diffusivity in Expandable Hydrogels: A Single-Molecule Study.

By repurposing the recently popularized expansion microscopy to control the meshwork size of hydrogels, we examine the size-dependent suppression of molecular diffusivity in the resultant tuned hydrogel nanomatrices over a wide range of polymer fractions of ∼0.14-7 wt %. With our recently developed single-molecule displacement/diffusivity mapping (SMdM) microscopy methods, we thus show that with a fixed meshwork size, larger molecules exhibit more impeded diffusion and that, for the same molecule, diffusion is progressively more suppressed as the meshwork size is reduced; this effect is more prominent for the larger molecules. Moreover, we show that the meshwork-induced obstruction of diffusion is uncoupled from the suppression of diffusion due to increased solution viscosities. Thus, the two mechanisms, respectively, being diffuser-size-dependent and independent, may separately scale down molecular diffusivity to produce the final diffusion slowdown in complex systems like the cell.

Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.

While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly extract the diffusion coefficient D from single-molecule images, and consequently enable super-resolved D spatial mapping. Working with single-molecule images recorded at a fixed framerate under typical single-molecule localization microscopy (SMLM) conditions, Pix2D exploits the often undesired yet evident motion blur, i.e., the convolution of single-molecule motion trajectory during the frame recording time with the diffraction-limited point spread function (PSF) of the microscope. Whereas the stochastic nature of diffusion imprints diverse diffusion trajectories to different molecules diffusing at the same given D, we construct a convolutional neural network (CNN) model that takes a stack of single-molecule images as the input and evaluates a D-value as the output. We thus validate robust D evaluation and spatial mapping with simulated data, and with experimental data successfully characterize D differences for supported lipid bilayers of different compositions and resolve gel and fluidic phases at the nanoscale.

Displacement Statistics of Unhindered Single Molecules Show no Enhanced Diffusion in Enzymatic Reactions.

Recent studies have sparked debate over whether catalytic reactions enhance the diffusion coefficients D of enzymes. Through high statistics of the transient (600 µs) displacements of unhindered single molecules freely diffusing in common buffers, we here quantify D for four enzymes under catalytic turnovers. We thus formulate how ∼ ±1% precisions may be achieved for D, and show no changes in diffusivity for catalase, urease, aldolase, and alkaline phosphatase under the application of wide concentration ranges of substrates. Our single-molecule approach thus overcomes potential limitations and artifacts underscored by recent studies to show no enhanced diffusion in enzymatic reactions.

Nanoparticle-based computing architecture for nanoparticle neural networks.

The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without restructuring the computer, we realized the nanoparticle-based VNA (NVNA) on a lipid chip for multiple executions of arbitrary molecular logic operations in the single chip without refabrication. In this system, nanoparticles on a lipid chip function as the hardware that features memory, processors, and output units, and DNA strands are used as the software to provide molecular instructions for the facile programming of logic circuits. NVNA enables a group of nanoparticles to form a feed-forward neural network, a perceptron, which implements functionally complete Boolean logic operations, and provides a programmable, resettable, scalable computing architecture and circuit board to form nanoparticle neural networks and make logical decisions.

Plasmonic Nanoparticle-Interfaced Lipid Bilayer Membranes.

Plasmonic nanoparticles are widely exploited in diverse bioapplications ranging from therapeutics to biosensing and biocomputing because of their strong and tunable light-matter interactions, facile and versatile chemical/biological ligand modifications, and biocompatibility. With the rapid growth of nanobiotechnology, understanding dynamic interactions between nanoparticles and biological systems at the molecular or single-particle level is becoming increasingly important for interrogating biological systems with functional nanostructures and for developing nanoparticle-based biosensors and therapeutic agents. Therefore, significant efforts have been devoted to precisely design and create nano-bio interfaces by manipulating the nanoparticles' size, shape, and surface ligand interactions with complex biological systems to maximize their performance and avoid unwanted responses, such as their agglomeration and cytotoxicity. However, investigating physicochemical interactions at the nano-bio interfaces in a quantitative and controllable manner remains challenging, as the interfaces involve highly complex networks between nanoparticles, biomolecules, and cells across multiple scales, each with a myriad of different chemical and biological interactions. A lipid bilayer is a membrane made of two layers of lipid molecules that forms a barrier around cells and plays structural and functional roles in diverse biological processes because they incorporate and present functional molecules (such as membrane proteins) with lateral fluidity. Plasmonic nanoparticles conjugated on lipid membranes provide reliable analytical labels and functional moieties that allow for studying and manipulating interactions between nanoparticles and molecules with single-particle resolution; they also serve as efficient tools for applying optical, mechanical, and thermal stimuli to biological systems, which stem from plasmonic properties. Recently, new opportunities have emerged by interfacing nanoparticle-modified lipid bilayers (NLBs) with complex systems such as molecular circuits and living systems. In this Account, we briefly review how plasmonic properties can be beneficially harnessed on lipid bilayer membranes to investigate the structures and functions of cellular membranes and to develop new platforms for biomedical applications. In particular, we discuss the versatility of supported lipid bilayers (SLBs), which are planar lipid bilayers on hydrophilic substrates, as dynamic biomaterials that provide lateral fluidity and cell membrane-like environments. We then summarize our efforts to create a quantitative analytical platform utilizing nanoparticles as active building blocks and SLBs as integrative substrates. Through this bottom-up approach, various functionalized nanoparticles have been introduced onto lipid bilayers to render nanoparticle-nanoparticle, nanoparticle-lipid bilayer, and biomolecule-lipid bilayer interfaces programmable. Our system provides a new class of tools for studying thermodynamics and kinetics in complex networks of nanostructures and for realizing unique applications in biosensing and biocomputing.

Biocomputing with Nanostructures on Lipid Bilayers.

Biocomputation is the algorithmic manipulation of biomolecules. Nanostructures, most notably DNA nanostructures and nanoparticles, become active substrates for biocomputation when modified with stimuli-responsive, programmable biomolecular ligands. This approach-biocomputing with nanostructures ("nano-bio computing")-allows autonomous control of matter and information at the nanoscale; their dynamic assemblies and beneficial properties can be directed without human intervention. Recently, lipid bilayers interfaced with nanostructures have emerged as a new biocomputing platform. This new nano-bio interface, which exploits lipid bilayers as a chemical circuit board for information processing, offers a unique reaction space for realizing nanostructure-based computation at a previously unexplored dimension. In this Concept, recent advances in nano-bio computing are briefly reviewed and the newly emerging concept of biocomputing with nanostructures on lipid bilayers is introduced.

Nano-bio-computing lipid nanotablet.

Using nanoparticles as substrates for computation enables algorithmic and autonomous controls of their unique and beneficial properties. However, scalable architecture for nanoparticle-based computing systems is lacking. Here, we report a platform for constructing nanoparticle logic gates and circuits at the single-particle level on a supported lipid bilayer. Our "lipid nanotablet" platform, inspired by cellular membranes that are exploited to compartmentalize and control signaling networks, uses a lipid bilayer as a chemical circuit board and nanoparticles as computational units. On a lipid nanotablet, a single-nanoparticle logic gate senses molecules in solution as inputs and triggers particle assembly or disassembly as an output. We demonstrate a set of Boolean logic operations, fan-in/fan-out of logic gates, and a combinational logic circuit such as a multiplexer. We envisage that our approach to modularly implement nanoparticle circuits on a lipid bilayer will create new paradigms and opportunities in molecular computing, nanoparticle circuits, and systems nanoscience.